





'''simple docstring'''


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate






@dataclass(frozen=UpperCAmelCase_    )
class     lowerCAmelCase__ (				UpperCAmelCase_    ):

       '''simple docstring'''
       _lowerCamelCase								=field(default="image-classification"      , metadata={"include_in_asdict_even_if_is_default": True}    )
       _lowerCamelCase								=Features({"image": Image()}    )
       _lowerCamelCase								=Features({"labels": ClassLabel}    )
       _lowerCamelCase								="image"
       _lowerCamelCase								="labels"

       def 			__snake_case      ( self					:				int       ,     a__					:				Union[str, Any]						):
              if self.label_column not in features:
                     raise ValueError(f"Column {self.label_column} is not present in features."						)
              if not isinstance(features[self.label_column]       ,     a__						):
                     raise ValueError(f"Column {self.label_column} is not a ClassLabel."						)
              UpperCAmelCase  =						copy.deepcopy(self						)
              UpperCAmelCase  =						self.label_schema.copy()
              UpperCAmelCase  =						features[self.label_column]
              UpperCAmelCase  =						label_schema
              return task_template



       @property
       def 			__snake_case      ( self					:				List[str]						):
              return {
                  self.image_column: "image",
                  self.label_column: "labels",
              }




"""simple docstring"""



import importlib

import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase        = OmegaConf.load(_A   )
    if display:
        print(yaml.dump(OmegaConf.to_container(_A   )   )   )
    return config


def 			_UpperCamelCase						(    _A       ,   _A=None       ,   _A=None   )     ->	List[Any]:





    """simple docstring"""

    if conf_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.yaml"""
    _UpperCAmelCase        = load_config(_A       ,   display=_A   )
    _UpperCAmelCase        = VQModel(**config.model.params   )
    if ckpt_path is None:
        _UpperCAmelCase        = """./model_checkpoints/vqgan_only.pt"""
    _UpperCAmelCase        = torch.load(_A       ,   map_location=_A   )
    if ".ckpt" in ckpt_path:
        _UpperCAmelCase        = sd["""state_dict"""]
    model.load_state_dict(_A       ,   strict=_A   )
    model.to(_A   )
    del sd
    return model


def 			_UpperCamelCase						(    _A       ,   _A   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = model.encode(_A   )
    print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}"""   )
    _UpperCAmelCase        = model.decode(_A   )
    return xrec


def 			_UpperCamelCase						(    _A       ,   _A=False   )     ->	Optional[Any]:





    """simple docstring"""

    _UpperCAmelCase     ,_UpperCAmelCase        = string.rsplit("""."""       ,   1   )
    if reload:
        _UpperCAmelCase        = importlib.import_module(_A   )
        importlib.reload(_A   )
    return getattr(importlib.import_module(_A       ,   package=_A   )       ,   cls   )


def 			_UpperCamelCase						(    _A   )     ->	str:





    """simple docstring"""

    if "target" not in config:
        raise KeyError("""Expected key `target` to instantiate."""   )
    return get_obj_from_str(config["""target"""]   )(**config.get("""params"""       ,   {}   )   )


def 			_UpperCamelCase						(    _A       ,   _A       ,   _A=True       ,   _A=True   )     ->	Union[str, Any]:





    """simple docstring"""

    _UpperCAmelCase        = instantiate_from_config(_A   )
    if sd is not None:
        model.load_state_dict(_A   )
    if gpu:
        model.cuda()
    if eval_mode:
        model.eval()
    return {"model": model}







def 			_UpperCamelCase						(    _A       ,   _A       ,   _A       ,   _A   )     ->	Optional[Any]:





    """simple docstring"""

    if ckpt:
        _UpperCAmelCase        = torch.load(_A       ,   map_location="""cpu"""   )
        _UpperCAmelCase        = pl_sd["""global_step"""]
        print(F"""loaded model from global step {global_step}."""   )
    else:
        _UpperCAmelCase        = {"""state_dict""": None}
        _UpperCAmelCase        = None
    _UpperCAmelCase        = load_model_from_config(config.model       ,   pl_sd["""state_dict"""]       ,   gpu=_A       ,   eval_mode=_A   )["""model"""]
    return model, global_step